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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# RHermes
<!-- badges: start -->
[![Codecov test coverage](https://codecov.io/gh/RogerGinBer/RHermes/branch/master/graph/badge.svg?token=HL73R4GHFJ)](https://codecov.io/gh/RogerGinBer/RHermes?branch=master)
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<img align = "right" style = "padding-left: 10%; padding-bottom: 10%; padding-top: 2%"
width = "250px" height = "285px" src = "https://i.postimg.cc/Tw9SvJ11/sticker-No-Bioc.png">
`RHermes` is a broad-scoped targeted metabolomics package designed to analyse
LC-MS and LC-MS/MS data to identify compounds in biological or environmental
samples.
The `RHermes` workflow works with both Orbitrap and qTOF instrument data and
comes with an interactive GUI to process and visualize the data.
Whether it's natural products, biomedical or enviormental samples, `RHermes` can
help you improve your matrix characterization. By selecting an appropiate
formula database, you can focus on just the compounds you are interested in and
improve your coverage.
With `RHermes` you can **see** the metabolites in your data and much
more. There's no need to manually calculate m/z's to plot the XIC of different
adducts: with the GUI you are just one click away from a metabolite-centric
plot.
For more info, check out the documentation [here](https://rogerginber.github.io/RHermes/)
and our article [here](https://www.nature.com/articles/s41592-021-01307-z).
## Installation
You can download the development version from
[GitHub](https://github.com/RogerGinBer/RHermes) with:
```{r, eval = FALSE}
if(!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("RogerGinBer/RHermes")
```
## Analyzing LC-MS data with RHermes
Once installed, you can use `RHermes` programmatically like this:
```{r example, eval = FALSE}
library(RHermes)
#Generate a Exp object
example <- RHermesExp()
#Set your formula and adduct database
example <- setDB(example, db = "hmdb")
#Process your MS1 files
example <- processMS1(example,
system.file("extdata", "MS1TestData.mzML",
package = "RHermes"))
#Generate SOIs
example <- findSOI(example, getSOIpar(), 1)
#Generate an IL (Inclusion List)
example <- generateIL(example, 1, ILParam())
```
With the generated inclusion list, you can export it and run a Parallel Reaction
Monitoring (PRM) MS2 experiment to reveal coeluting isomers or use any other MS2 mode
you see fit.
Or start the interactive GUI typing:
```{r, eval = FALSE}
RHermesGUI()
```
In the GUI you will find abundant help pages to guide you along the processing :+1:
Please check the User Guide [vignette](https://rogerginber.github.io/RHermes/articles/RHermes_UserGuide.html) for more detailed info and real examples.
## Database availability
We have compiled some molecular formula open databases ready to be used with RHermes for all sorts of samples: HMDB, ChEBI, NORMAN, LipidMaps, COCONUT, etc. They are freely available at [this Zenodo repository](https://zenodo.org/record/5025560).
## Bug reporting
Suggestions and bug reports are more than welcome at: https://github.com/RogerGinBer/RHermes/issues
## Citation
Please cite this package as:
Giné, R., Capellades, J., Badia, J.M. et al. HERMES: a molecular-formula-oriented method to target the metabolome. Nat Methods 18, 1370–1376 (2021). https://doi.org/10.1038/s41592-021-01307-z